
import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCamelCase_    :			List[str]    =	{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
							lowerCamelCase_    :			Optional[int]    =	"""hopper-medium-v2"""
							lowerCamelCase_    :			Any    =	gym.make(env_name)

							lowerCamelCase_    :			List[Any]    =	ValueGuidedRLPipeline.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32""",
							    env=env,
							)

							env.seed(0)
							lowerCamelCase_    :			str    =	env.reset()
							lowerCamelCase_    :			str    =	0
							lowerCamelCase_    :			str    =	0
							lowerCamelCase_    :			List[str]    =	10_00
							lowerCamelCase_    :			int    =	[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCamelCase_    :			str    =	pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCamelCase_ ,							lowerCamelCase_ ,							lowerCamelCase_ ,							lowerCamelCase_    :			Any    =	env.step(denorm_actions)
																					lowerCamelCase_    :			str    =	env.get_normalized_score(total_reward)

																					# update return
																					total_reward += reward
																					total_score += score
																					print(
																					    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
																					    F""" {total_score}"""
																					)

																					# save observations for rendering
																					rollout.append(next_observation.copy())

																					lowerCamelCase_    :			Union[str, Any]    =	next_observation
							except KeyboardInterrupt:
														pass

							print(F"""Total reward: {total_reward}""")








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
							from .autoencoder_kl import AutoencoderKL
							from .controlnet import ControlNetModel
							from .dual_transformer_ad import DualTransformeraDModel
							from .modeling_utils import ModelMixin
							from .prior_transformer import PriorTransformer
							from .ta_film_transformer import TaFilmDecoder
							from .transformer_ad import TransformeraDModel
							from .unet_ad import UNetaDModel
							from .unet_ad import UNetaDModel
							from .unet_ad_condition import UNetaDConditionModel
							from .unet_ad_condition import UNetaDConditionModel
							from .vq_model import VQModel

if is_flax_available():
							from .controlnet_flax import FlaxControlNetModel
							from .unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .vae_flax import FlaxAutoencoderKL






